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检索条件"任意字段=5th International Conference on Medical Image Computing and Computer-Assisted Intervention"

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cOOpD: Reformulating COPD Classification on Chest CT Scans as Anomaly Detection Using Contrastive Representations 26th

cOOpD: Reformulating COPD Classification on Chest CT Scans a...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Almeida, Silvia D. Lueth, Carsten T. Norajitra, Tobias Wald, Tassilo Nolden, Marco Jaeger, Paul F. Heussel, Claus P. Biederer, Juergen Weinheimer, Oliver Maier-Hein, Klaus H. German Canc Res Ctr Div Med Image Comp Heidelberg Germany Heidelberg Univ Med Fac Heidelberg Germany German Ctr Lung Res DZL Translat Lung Res Ctr Heidelberg TLRC Heidelberg Germany German Canc Res Ctr Interact Machine Learning Grp Heidelberg Germany German Canc Res Ctr Helmholtz Imaging Heidelberg Germany Thoraxklin Univ Hosp Diagnost & Intervent Radiol Nucl Med Heidelberg Germany Univ Hosp Diagnost & Intervent Radiol Heidelberg Germany

ISBN: (纸本)9783031439032;9783031439049

Classification of heterogeneous diseases is challenging due to their complexity, variability of symptoms and imaging findings. Chronic Obstructive Pulmonary Disease (COPD) is a prime example, being underdiagnosed despite being the third leading cause of death. Its sparse, diffuse and heterogeneous appearance on computed tomography challenges supervised binary classification. We reformulate COPD binary classification as an anomaly detection task, proposing cOOpD: heterogeneous pathological regions are detected as Out-of-Distribution (OOD) from normal homogeneous lung regions. To this end, we learn representations of unlabeled lung regions employing a self-supervised contrastive pretext model, potentially capturing specific characteristics of diseased and healthy unlabeled regions. A generative model then learns the distribution of healthy representations and identifies abnormalities (stemming from COPD) as deviations. Patient-level scores are obtained by aggregating region OOD scores. We show that cOOpD achieves the best performance on two public datasets, with an increase of 8.2% and 7.7% in terms of AUROC compared to the previous supervised state-of-the-art. Additionally, cOOpD yields well-interpretable spatial anomaly maps and patient-level scores which we show to be of additional value in identifying individuals in the early stage of progression. Experiments in artificially designed real-world prevalence settings further support that anomaly detection is a powerful way of tackling COPD classification. Code is at https://***/MIC-DKFZ/cOOpD.

关键词： COPD classification Anomaly detection Contrastive learning

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You've Got Two Teachers: Co-evolutionary image and Report Distillation for Semi-supervised Anatomical Abnormality Detection in Chest X-Ray 26th

You've Got Two Teachers: Co-evolutionary Image and Report Di...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Sun, Jinghan Wei, Dong Xu, Zhe Lu, Donghuan Liu, Hong Wang, Liansheng Zheng, Yefeng Xiamen Univ Natl Inst Data Sci Hlth & Med Xiamen Peoples R China Tencent Healthcare Shenzhen Co LTD Tencent Jarvis Lab Shenzhen Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9783031439063;9783031439070

Chest X-ray (CXR) anatomical abnormality detection aims at localizing and characterising cardiopulmonary radiological findings in the radiographs, which can expedite clinical workflow and reduce observational oversights. Most existing methods attempted this task in either fully supervised settings which demanded costly mass perabnormality annotations, or weakly supervised settings which still lagged badly behind fully supervised methods in performance. In this work, we propose a co-evolutionary image and report distillation (CEIRD) framework, which approaches semi-supervised abnormality detection in CXR by grounding the visual detection results with text-classified abnormalities from paired radiology reports, and vice versa. Concretely, based on the classical teacher-student pseudo label distillation (TSD) paradigm, we additionally introduce an auxiliary report classification model, whose prediction is used for report-guided pseudo detection label refinement (RPDLR) in the primary vision detection task. Inversely, we also use the prediction of the vision detection model for abnormality-guided pseudo classification label refinement (APCLR) in the auxiliary report classification task, and propose a co-evolution strategy where the vision and report models mutually promote each other with RPDLR and APCLR performed alternatively. To this end, we effectively incorporate the weak supervision by reports into the semi-supervised TSD pipeline. Besides the cross-modal pseudo label refinement, we further propose an intra-image-modal self-adaptive non-maximum suppression, where the pseudo detection labels generated by the teacher vision model are dynamically rectified by high-confidence predictions by the student. Experimental results on the public MIMIC-CXR benchmark demonstrate CEIRD's superior performance to several up-to-date weakly and semi-supervised methods.

关键词： Anatomical abnormality detection Semi-supervised learning Co-evolution Visual and textual grounding Chest X-ray

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image Quality Transfer of Diffusion MRI Guided By High-Resolution Structural MRI 15th

Image Quality Transfer of Diffusion MRI Guided By High-Reso...

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15th international Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

作者： Cicimen, Alp G. Tregidgo, Henry F. J. Figini, Matteo Messaritaki, Eirini B. McNabb, Carolyn Palombo, Marco Evans, C. John Cercignani, Mara Jones, Derek K. C. Alexander, Daniel Centre for Medical Image Computing University College London London United Kingdom Cardiff University Brain Research Imaging Centre Cardiff University Cardiff United Kingdom

ISBN: (纸本)9783031869198

Prior work on the image Quality Transfer on Diffusion MRI (dMRI) has shown significant improvement over traditional interpolation methods. However, the difficulty in obtaining ultra-high resolution Diffusion MRI scans poses a problem in training neural networks to obtain high-resolution dMRI scans. Here we hypothesise that the inclusion of structural MRI images, which can be acquired at much higher resolutions, can be used as a guide to obtaining a more accurate high-resolution dMRI output. To test our hypothesis, we have constructed a novel framework that incorporates structural MRI scans together with dMRI to obtain high-resolution dMRI scans. We set up tests which evaluate the validity of our claim through various configurations and compare the performance of our approach against a unimodal approach. Our results show that the inclusion of structural MRI scans do lead to an improvement in high-resolution image prediction when T1w data is incorporated into the model input. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image coding

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QID2: An image-Conditioned Diffusion Model for Q-Space Up-Sampling of DWI Data 15th

QID2: An Image-Conditioned Diffusion Model for Q-Space Up-S...

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作者： Chen, Zijian Wang, Jueqi Venkataraman, Archana Department of Electrical and Computer Engineering Boston University Boston United States

ISBN: (纸本)9783031869198

We propose an image-conditioned diffusion model to estimate high angular resolution diffusion weighted imaging (DWI) from a low angular resolution acquisition. Our model, which we call QID2, takes as input a set of low angular resolution DWI data and uses this information to estimate the DWI data associated with a target gradient direction. We leverage a U-Net architecture with cross-attention to preserve the positional information of the reference images, further guiding the target image generation. We train and evaluate QID2 on single-shell DWI samples curated from the Human Connectome Project (HCP) dataset. Specifically, we sub-sample the HCP gradient directions to produce low angular resolution DWI data and train QID2 to reconstruct the missing high angular resolution samples. We compare QID2 with two state-of-the-art GAN models. Our results demonstrate that QID2 not only achieves higher-quality generated images, but it consistently outperforms state-of-the-art baseline methods in downstream tensor estimation across multiple metrics and in generalizing to downsampling scenario during testing. Taken together, this study highlights the potential of diffusion models, and QID2 in particular, for q-space up-sampling, thus offering a promising toolkit for clinical and research applications. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep Learning Diffusion Models Diffusion Weighted Imaging Q-Space Up-sampling Tensor Reconstruction

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TransFusion: Multi-view Divergent Fusion for medical image Segmentation with Transformers 25th

TransFusion: Multi-view Divergent Fusion for Medical Image S...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Liu, Di Gao, Yunhe Zhangli, Qilong Han, Ligong He, Xiaoxiao Xia, Zhaoyang Wen, Song Chang, Qi Yan, Zhennan Zhou, Mu Metaxas, Dimitris Rutgers State Univ Dept Comp Sci New Brunswick NJ 08901 USA SenseBrain Res San Jose CA USA

ISBN: (纸本)9783031164439;9783031164422

Combining information from multi-view images is crucial to improve the performance and robustness of automated methods for disease diagnosis. However, due to the non-alignment characteristics of multi-view images, building correlation and data fusion across views largely remain an open problem. In this study, we present TransFusion, a Transformer-based architecture to merge divergent multi-view imaging information using convolutional layers and powerful attention mechanisms. In particular, the Divergent Fusion Attention (DiFA) module is proposed for rich cross-view context modeling and semantic dependency mining, addressing the critical issue of capturing long-range correlations between unaligned data from different image views. We further propose the Multi-Scale Attention (MSA) to collect global correspondence of multi-scale feature representations. We evaluate TransFusion on the Multi-Disease, Multi-View & Multi-Center Right Ventricular Segmentation in Cardiac MRI (M&Ms-2) challenge cohort. TransFusion demonstrates leading performance against the state-of-the-art methods and opens up new perspectives for multi-view imaging integration towards robust medical image segmentation.

关键词： Cardiac MRI Segmentation Deep learning

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Scribble-Supervised medical image Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels Supervision 25th

Scribble-Supervised Medical Image Segmentation via Dual-Bran...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Luo, Xiangde Hu, Minhao Liao, Wenjun Zhai, Shuwei Song, Tao Wang, Guotai Zhang, Shaoting Univ Elect Sci & Technol China Chengdu Peoples R China Shanghai Lab Shanghai Peoples R China SenseTime Res Shanghai Peoples R China

ISBN: (纸本)9783031164316;9783031164309

medical image segmentation plays an irreplaceable role in computer-assisted diagnosis, treatment planning and following-up. Collecting and annotating a large-scale dataset is crucial to training a powerful segmentation model, but producing high-quality segmentation masks is an expensive and time-consuming procedure. Recently, weakly-supervised learning that uses sparse annotations (points, scribbles, bounding boxes) for network training has achieved encouraging performance and shown the potential for annotation cost reduction. However, due to the limited supervision signal of sparse annotations, it is still challenging to employ them for networks training directly. In this work, we propose a simple yet efficient scribble-supervised image segmentation method and apply it to cardiac MRI segmentation. Specifically, we employ a dual-branch network with one encoder and two slightly different decoders for image segmentation and dynamically mix the two decoders' predictions to generate pseudo labels for auxiliary supervision. By combining the scribble supervision and auxiliary pseudo labels supervision, the dual-branch network can efficiently learn from scribble annotations end-to-end. Experiments on the public ACDC dataset show that our method performs better than current scribble-supervised segmentation methods and also outperforms several semi-supervised segmentation methods. Code is available: https://***/HiLab-git/WSL4MIS.

关键词： Weakly-supervised learning Scribble annotation Pseudo labels

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You Don't Have to Be Perfect to Be Amazing: Unveil the Utility of Synthetic images 26th

You Don't Have to Be Perfect to Be Amazing: Unveil the Utili...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Xing, Xiaodan Felder, Federico Nan, Yang Papanastasiou, Giorgos Walsh, Simon Yang, Guang Imperial Coll London Natl Heart & Lung Inst London England Royal Brompton Hosp London England Imperial Coll London Bioengn Dept London W12 7SL England Imperial Coll London Imperial X London W12 7SL England Univ Essex Colchester Essex England Imperial Coll London Natl Heart & Lung Inst London SW7 2AZ England Royal Brompton Hosp Cardiovasc Res Ctr London SW3 6NP England Kings Coll London Sch Biomed Engn & Imaging Sci London WC2R 2LS England

ISBN: (纸本)9783031439032;9783031439049

Synthetic images generated from deep generative models have the potential to address data scarcity and data privacy issues. the selection of synthesis models is mostly based on image quality measurements, and most researchers favor synthetic images that produce realistic images, i.e., images with good fidelity scores, such as low Frechet Inception Distance (FID) and high Peak Signal-To-Noise Ratio (PSNR). However, the quality of synthetic images is not limited to fidelity, and a wide spectrum of metrics should be evaluated to comprehensively measure the quality of synthetic images. In addition, quality metrics are not truthful predictors of the utility of synthetic images, and the relations between these evaluation metrics are not yet clear. In this work, we have established a comprehensive set of evaluators for synthetic images, including fidelity, variety, privacy, and utility. By analyzing more than 100k chest X-ray images and their synthetic copies, we have demonstrated that there is an inevitable trade-off between synthetic image fidelity, variety, and privacy. In addition, we have empirically demonstrated that the utility score does not require images with both high fidelity and high variety. For intra- and cross-task data augmentation, mode-collapsed images and low-fidelity images can still demonstrate high utility. Finally, our experiments have also showed that it is possible to produce images with both high utility and privacy, which can provide a strong rationale for the use of deep generative models in privacy-preserving applications. Our study can shore up comprehensive guidance for the evaluation of synthetic images and elicit further developments for utility-aware deep generative models in medical image synthesis.

关键词： Synthetic Data Augmentation medical image Synthesis

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Uncertainty Aware Sampling Framework of Weak-Label Learning for Histology image Classification 25th

Uncertainty Aware Sampling Framework of Weak-Label Learning ...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Aljuhani, Asmaa Casukhela, Ishya Chan, Jany Liebner, David Machiraju, Raghu Ohio State Univ Dept Comp Sci & Engn Columbus OH USA Ohio State Univ Dept Biomed Informat Coll Med Columbus OH USA Ohio State Univ Dept Pathol Coll Med Columbus OH USA Ohio State Univ Div Med Oncol Dept Internal Med Columbus OH USA

ISBN: (纸本)9783031164347;9783031164330

Advances in digital pathology and deep learning have enabled robust disease classification, better diagnosis, and prognosis. In real-world settings, readily available and inexpensive image-level labels from pathology reports are weak, which seriously degrades the performance of deep learning models. Weak image-level labels do not represent the complexity and heterogeneity of the analyzed WSIs. this work presents an importance-based sampling framework for robust histopathology image analysis, Uncertainty-Aware Sampling Framework (UASF). Our experiments demonstrate the effectiveness of UASF when used to grade a highly heterogeneous subtype of soft tissue sarcomas. Furthermore, our proposed model achieves better accuracy when compared to the baseline models by sampling the most relevant tiles.

关键词： Histopathology image analysis Uncertainty estimation Weak label learning Noisy label detection

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Translating Clinical Delineation of Diabetic Foot Ulcers into Machine Interpretable Segmentation 4th

Translating Clinical Delineation of Diabetic Foot Ulcers in...

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4th Diabetic Foot Ulcers Grand Challenge, DFUC 2024 held in Conjunction 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

作者： Kendrick, Connah Cassidy, Bill Pappachan, Joseph M. O’Shea, Claire Fernandez, Cornelious J. Chacko, Elias Jacob, Koshy Reeves, Neil D. Yap, Moi Hoon Department of Computing and Mathematics Manchester Metropolitan University ManchesterM1 5GD United Kingdom Lancashire Teaching Hospitals NHS Foundation Trust PrestonPR2 9HT United Kingdom Waikato District Health Board Hamilton3240 New Zealand United Lincolnshire Hospitals NHS Trust LincolnshireLN2 5QY United Kingdom Jersey General Hospital JerseyJE1 3QS United Kingdom Eastbourne District General Hospital East Sussex EastbourneBN21 2UD United Kingdom Lancaster University LancasterLA1 4YW United Kingdom

ISBN: (纸本)9783031808708

Diabetic foot ulcer (DFU) is a severe condition that requires close monitoring and management. Experienced clinical staff are best-positioned to provide ground truth annotations to train machine learning methods to auto-delineate the ulcer. In this paper, we revisit the largest DFU dataset for segmentation that was used in the Diabetic Foot Ulcer Grand Challenge 2022 (DFUC 2022) and provide further analysis. the DFUC 2022 segmentation dataset provides clinical expert delineation of ulcer regions. We assess whether the clinical delineations are easily machine interpretable by deep learning networks or if image-processing refined contours should be used. this paper demonstrates that image processing using refined delineations as ground truth can provide better agreement with machine-predicted results. With the in-depth understanding and observation of baseline models, we provide insights to guide future development in DFU segmentation. this dataset continues to serve as the challenge dataset for DFUC 2024, and it is available at: https://***/. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

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A Comparative Study of ResNet50, EfficientNetB7, InceptionV3 and VGG16 models in Lung Cancer Prediction 5

A Comparative Study of ResNet50, EfficientNetB7, InceptionV3...

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5th IEEE international conference on Advances in computing, Communication Control and Networking, ICAC3N 2023

作者： Gupta, Vinayak Gupta, Utkarsh Anjana, Rahul Galgotias University School Of Computer Science And Engineering Greater Noida India

ISBN: (纸本)9798350330861

the society is thriving in uncertain times wherein any disease can get hold of the human body at any point of time leading to major consequences, the untimely detection can also lead to an increase of medical expenses at the cost of the life of a human. It is seen that cancer is another menace which haunts the society with lung cancer being the most common because of the smoking habits of the masses, lung cancer effects the air passages of a person inside their lungs leading to the worst-case scenario of a cancer death. To prevent such scenarios, the paper proposes this research to be able to detect the presence of lung cancer in a person with the help of images provided by CT scan of a person, the proposed methodology would work by first refining the images with the help of convolutional neural network (CNN) architecture and then the framework would study the images to detect the nodules of the lung, compare them to be malignant or nonhazardous thus predicting the possibility of lung cancer. © 2023 IEEE.

关键词： image segmentation

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